To train a deblurring network, an appropriate dataset with paired blurry and sharp images is essential. Existing datasets collect blurry images either synthetically by aggregating consecutive sharp frames or using sophisticated camera systems to capture real blur. However, these methods offer limited diversity in blur types (blur trajectories) or require extensive human effort to reconstruct large-scale datasets, failing to fully reflect real-world blur scenarios. To address this, we propose GS-Blur, a dataset of synthesized realistic blurry images created using a novel approach. To this end, we first reconstruct 3D scenes from multi-view images using 3D Gaussian Splatting (3DGS), then render blurry images by moving the camera view along the randomly generated motion trajectories. By adopting various camera trajectories in reconstructing our GS-Blur, our dataset contains realistic and diverse types of blur, offering a large-scale dataset that generalizes well to real-world blur. Using GS-Blur with various deblurring methods, we demonstrate its ability to generalize effectively compared to previous synthetic or real blur datasets, showing significant improvements in deblurring performance.
在训练去模糊网络时，拥有成对的模糊和清晰图像的合适数据集至关重要。现有数据集通常通过合成方法（叠加连续的清晰帧）或使用复杂的摄像系统捕捉真实模糊图像。然而，这些方法在模糊类型（如模糊轨迹）的多样性上有限，或需要大量人力来构建大规模数据集，难以充分反映真实世界中的模糊场景。为此，我们提出了GS-Blur，这是一个利用新方法合成的逼真模糊图像数据集。具体而言，我们首先利用多视角图像通过三维高斯分裂（3DGS）重建3D场景，然后通过沿随机生成的运动轨迹移动摄像机视角来渲染模糊图像。通过在GS-Blur的构建中采用不同的摄像机轨迹，我们的数据集包含了逼真且多样化的模糊类型，提供了一个适用于真实世界模糊的高泛化性大规模数据集。使用GS-Blur进行去模糊实验，与之前的合成或真实模糊数据集相比，我们展示了其在去模糊性能上的显著提升。